• Deploying Principal-Vetted talent directly into our partners' technical teams • Operating as an agile, embedded expert to drive critical initiatives forward rapidly

• Design and maintain end-to-end ML pipelines from data ingestion to model deployment • Operate model registries, feature stores, and experiment tracking (MLflow, W&B) • Build scalable model serving infrastructure on Kubernetes and cloud platforms • Implement CI/CD workflows for ML models, including testing and rollback strategies • Monitor production models — drift detection, alerting, and retraining pipelines • Collaborate with data scientists and platform engineers to ship ML solutions faster

• You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers. • You will build models that automate refunds, getting money back to our customers faster. • You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs. • You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. • You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.

• You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers. • You will build models that automate refunds, getting money back to our customers faster. • You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs. • You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. • You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.